Turbine set online fault early warning method based on abnormality searching and combination forecasting

Abstract

The invention discloses a turbine set online fault early warning method based on abnormality searching and combination forecasting, and belongs to the technical field of electric system early warning. The turbine set online fault early warning method includes the steps of carrying out input initializing processing responsible for segmenting an input parameter time sequence in a standardization mode, and extracting a sequence characteristic mode; carrying out abnormality characteristic boundary training: obtaining an abnormality searching reference standard by training normal state parameters; carrying out abnormality searching: determining an abnormality sequence set by searching characteristic boundary crossing; identifying an abnormality change trend through regression analysis to obtain abnormality analysis of an abnormality distribution change rule; building a forecasting model to carry out trend forecasting on abnormal changes; carrying out early warning output according to the forecasting result in cooperation with the corresponding relation between abnormality parameters and fault symptoms. According to the turbine set online fault early warning method, the defect that in traditional monitoring analysis, only a limiting value theory is used, the abnormality can not be completely identified is overcome, the abnormality early warning accuracy and the abnormality early warning depth are improved, and beneficial evidences are provided for unit fault causes and responsibility ascription.

Description

technical field [0001] The invention relates to the technical field of early warning of power systems, in particular to an online fault early warning method for steam turbine units based on abnormal search and combined prediction. Specifically, thermal power plants and nuclear power plants need identification, analysis and early warning technology for vibration and process signals of steam turbine units or large-scale rotary machinery. Background technique [0002] In the fierce market competition, each power generation enterprise seeks better development and strives to comprehensively enhance the comprehensive competitiveness of the enterprise. The most important means is to develop or introduce operating equipment with international advanced level. With the increasingly complex structure and functions of operating equipment in large and medium-sized enterprises, enterprises have higher and higher requirements for normal, safe and stable operation of equipment. In terms of ...

AI Technical Summary

Problems solved by technology

Obviously, compared with modern high-precision data acquisition technology, the relatively backward monitoring and analysis technology has seriously lagged behind; the application of sensor monitoring technology and vibration analysis and diagnosis technology in related fields has basically realized the condition monitoring and fault diagnosis of large rotating machinery, but Fault diagnosis and troubleshooting lack predictability, have a certain functional lag, and cannot realize early warning of steam turbine unit operation faults

At the same time, the traditional signal acquisition system often focuses on the acquisition and analysis of the global signal, but lacks the secondary search and mining analysis of the transaction data hidden in the signal

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